Choi, Junhyuk Kim, Keun Tae Jeong, Ji Hyeok Kim, Laehyun Lee, Song Joo Kim, Hyungmin 2024-01-19T16:02:19Z 2024-01-19T16:02:19Z 2021-09-02 2020-12 1424-8220 https://pubs.kist.re.kr/handle/201004/117763 This study aimed to develop an intuitive gait-related motor imagery (MI)-based hybrid brain-computer interface (BCI) controller for a lower-limb exoskeleton and investigate the feasibility of the controller under a practical scenario including stand-up, gait-forward, and sit-down. A filter bank common spatial pattern (FBCSP) and mutual information-based best individual feature (MIBIF) selection were used in the study to decode MI electroencephalogram (EEG) signals and extract a feature matrix as an input to the support vector machine (SVM) classifier. A successive eye-blink switch was sequentially combined with the EEG decoder in operating the lower-limb exoskeleton. Ten subjects demonstrated more than 80% accuracy in both offline (training) and online. All subjects successfully completed a gait task by wearing the lower-limb exoskeleton through the developed real-time BCI controller. The BCI controller achieved a time ratio of 1.45 compared with a manual smartwatch controller. The developed system can potentially be benefit people with neurological disorders who may have difficulties operating manual control. English Multidisciplinary Digital Publishing Institute (MDPI) Developing a Motor Imagery-Based Real-Time Asynchronous Hybrid BCI Controller for a Lower-Limb Exoskeleton Article 10.3390/s20247309 1 Sensors, v.20, no.24 Sensors 20 24 Y scie scopus 000603307100001 2-s2.0-85098185342 Chemistry, Analytical Engineering, Electrical & Electronic Instruments & Instrumentation Chemistry Engineering Instruments & Instrumentation Article SINGLE-TRIAL EEG BRAIN INTERFACES OPERATION SCIENCE hybrid BCI EEG motor imagery FBCSP lower-limb exoskeleton